1. Field of the Invention
The present invention relates to a person identification apparatus and method. More specifically, the invention relates to an apparatus and method for extracting feature points from a face image to identify a person.
2. Description of the Related Art
Systems for automatically identifying people have many uses, including enhancing security. Basic processing of a personal identification system of the prior art is as follows.
1. Extraction of the face area from a face image of a person PA0 2. Extraction of feature points such as eyes, nose, or mouth from the face area and PA0 3. Discrimination processing
Discrimination processing falls into one of two categories. In the first method, a feature vector is generated according to the position, shape, and size of a feature point and a similarity value between the feature vector and a plurality of previously registered dictionary vectors. The plurality of dictionary vectors correspond to the object persons to be confirmed. Accordingly, the person who corresponds to the face image is identified as the object person having the highest similarity value.
In the second method, the position and size of the feature points in the face image are normalized by two-dimensional affine conversion. This normalized image is compared with a dictionary normalized image which corresponds to the object person. Like the first method, the person who corresponds to the face image is identified as the object person having the highest similarity value.
In the two above-mentioned methods, the similarity value is calculated as a statistical distance such as a relative value between images or the Euclidean distance in characteristic space. In discriminative processing, various kinds of pattern recognition theories are used for character identification. In this case, a critical factor for achieving quality discrimination is the ability to extract the feature points. In the first method, the effect of extracting the feature points is large because the position, shape and size of each feature point is used as an element of the feature vector. In the second method, the effect of extracting the feature points is also large because the quality of discrimination depends on minuteness of the normalized image, and the dictionary image in the case of calculating the similarity value.
A conventional method for extracting the feature points is based on edge information in the image. By using an edge operator, the edge in the face area is extracted and eyes, nose, and mouth are detected according to an edge model. Because the shape of the face image is three-dimensionally transformed, it is difficult to detect a clear edge from the face image. Therefore, this method is only applied in cases where lighting conditions are good.
Using a template to extract feature points is another method used. In this method, the feature points are extracted by using a template (e.g., a dictionary pattern) to match a previously registered eye, nose, or mouth. Only patterns of areas such as the eyes, nose, or mouth are used for matching. Therefore, it is difficult to discriminate one area from another similar area. To process many different positions and sizes of the eyes, nose, or mouth, numerous templates and data associated with each are needed to provide an adequate sample for comparison with features of the face under analysis. Therefore, when using this method the number of calculations required greatly increases as the scope of the search area for recognizable faces increases.
A problem with this second method of extracting feature points concerns proper extraction of the feature points using the location information taken from the face area. For example, a method to extract a feature point by connecting each feature point using a virtual spring assumes that the eyes, nose, and mouth are located on the face in a normal distribution. However, in this method, it is difficult to distinguish partial errors because the connected shape of the virtual spring may represent an entire facial feature. Mistaking the edge of an eyebrow for a black eye is an example of a partial error.
In yet another method, feature points are correctly extracted by minimizing the value of an evaluation function. A problem associated with this method, is setting the weight of the evaluation function. This method requires repeated calculations for processing the minimization.